Abstract
Abstract
The rapid diffusion of artificial intelligence (AI) and digital platforms is fundamentally transforming organizational investment management. While prior information systems (IS) literature has largely focused on technological capabilities, less attention has been devoted to how managerial actors cognitively interpret and operationalize these systems in dynamic environments. Addressing this gap, this study explores how middle managers perceive the current and evolving role of AI in capital allocation and investment decision-making. Utilizing a qualitative exploratory design, we conducted semi-structured interviews with 42 middle managers working specifically within the financial sector of multinational organizations. The thematic analysis identifies five major transformation domains: augmented decision intelligence, adaptive capital allocation, decision velocity governance, trust and explainability and leadership transformation. The findings reveal a paradigm shift from static, periodic budgeting toward continuous, data-driven resource orchestration. Managers consistently describe AI as a cognitive extension that augments rather than substitutes human judgment, suggesting that future competitive advantage depends less on algorithmic sophistication and more on data quality, organizational adaptability and learning capabilities. Grounded in cybernetic leadership theory, dynamic capability theory and the human-in-the-loop paradigm, this study develops the Cybernetic Middle Management Decision Model (CMMDM). The model conceptualizes managerial decision-making as a recursive cycle of intelligence acquisition, contextual interpretation, adaptive decision commitment and recursive learning feedback, supported by trust, explainability and data quality as enabling conditions. The study contributes to the information systems and management literature by proposing a novel decision framework that explains how AI-mediated investment environments reshape managerial judgment, capital allocation and organizational learning. The findings further highlight how AI-enabled decision systems may contribute not only to investment effectiveness but also to broader questions of financial resilience, governance and systemic stability.